k-means clustering

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im <- readImage("papSmear.jpg")
dim(im)

## [1] 493 335   3

plot(im) # raster method means within R

# reshape image into a data frame
df = data.frame(
  red = matrix(im[,,1], ncol=1),
  green = matrix(im[,,2], ncol=1),
  blue = matrix(im[,,3], ncol=1)
)
str(df)

## 'data.frame':    165155 obs. of  3 variables:
##  $ red  : num  0.886 0.906 0.933 0.953 0.957 ...
##  $ green: num  0.694 0.714 0.741 0.761 0.765 ...
##  $ blue : num  0.71 0.729 0.757 0.776 0.78 ...

### compute the k-means clustering
K = kmeans(df,5)
str(K)

## List of 9
##  $ cluster     : int [1:165155] 3 3 4 4 4 4 4 4 4 4 ...
##  $ centers     : num [1:5, 1:3] 0.707 0.502 0.885 0.966 0.61 ...
##   ..- attr(*, "dimnames")=List of 2
##   .. ..$ : chr [1:5] "1" "2" "3" "4" ...
##   .. ..$ : chr [1:3] "red" "green" "blue"
##  $ totss       : num 8016
##  $ withinss    : num [1:5] 99.8 77.6 167 162.5 111.5
##  $ tot.withinss: num 618
##  $ betweenss   : num 7398
##  $ size        : int [1:5] 19219 5534 18104 105805 16493
##  $ iter        : int 6
##  $ ifault      : int 0
##  - attr(*, "class")= chr "kmeans"

head(K$centers)

##         red     green      blue
## 1 0.7068195 0.6314813 0.6412725
## 2 0.5015547 0.3339265 0.4417115
## 3 0.8852673 0.6515596 0.7181804
## 4 0.9664161 0.8012112 0.8026724
## 5 0.6097807 0.5254588 0.5571255

df$label = K$cluster
head(df)

### Replace the color of each pixel in the image with the mean 
### R,G, and B values of the cluster in which the pixel resides:

# get the coloring
colors = data.frame(
  label = 1:nrow(K$centers), 
  R = K$centers[,"red"],
  G = K$centers[,"green"],
  B = K$centers[,"blue"]
)
dim(colors)

## [1] 5 4

colors

# merge color codes on to df
# IMPORTANT: we must maintain the original order of the df after the merge!
df$order = 1:nrow(df)
df = merge(df, colors)
df = df[order(df$order),]
df$order = NULL

head(df)

Reshape our data frame back into an image

# get mean color channel values for each row of the df.
R <- matrix(df$R, nrow=dim(im)[1])
G <- matrix(df$G, nrow=dim(im)[1])
B <- matrix(df$B, nrow=dim(im)[1])
  
# reconstitute the segmented image in the same shape as the input image
im.segmented <- array(dim=dim(im))
im.segmented[,,1] = R
im.segmented[,,2] = G
im.segmented[,,3] = B
#im.segmented <- rotate(im.segmented, 90)
#im.segmented <- flop(im.segmented )
#dim(im.segmented)
#str(im.segmented)
#class(im.segmented)
#EBImage::writeImage(im.segmented, files = "im.segmented.jpg")
# View the result
#layout(t(1:2))

## convert matrix to Image
im.segmented <- Image(im.segmented, colormode=Color)

class(im.segmented)

## [1] "Image"
## attr(,"package")
## [1] "EBImage"

class(im)

## [1] "Image"
## attr(,"package")
## [1] "EBImage"

plot(im.segmented)

plot(im)

dim(im.segmented)

## [1] 493 335   3

dim(im)

## [1] 493 335   3

Function

get_kmean_image <- function(file, k){
  im <- readImage(file)
  # reshape image into a data frame
df = data.frame(
  red = matrix(im[,,1], ncol=1),
  green = matrix(im[,,2], ncol=1),
  blue = matrix(im[,,3], ncol=1)
)

### compute the k-means clustering
K = kmeans(df,k)
df$label = K$cluster
 
### Replace the color of each pixel in the image with the mean 
### R,G, and B values of the cluster in which the pixel resides:

# get the coloring
colors = data.frame(
  label = 1:nrow(K$centers), 
  R = K$centers[,"red"],
  G = K$centers[,"green"],
  B = K$centers[,"blue"]
)

# merge color codes on to df
# IMPORTANT: we must maintain the original order of the df after the merge!
df$order = 1:nrow(df)
df = merge(df, colors)
df = df[order(df$order),]
df$order = NULL


# get mean color channel values for each row of the df.
R <- matrix(df$R, nrow=dim(im)[1])
G <- matrix(df$G, nrow=dim(im)[1])
B <- matrix(df$B, nrow=dim(im)[1])
  
# reconstitute the segmented image in the same shape as the input image
im.segmented <- array(dim=dim(im))
im.segmented[,,1] = R
im.segmented[,,2] = G
im.segmented[,,3] = B

## convert matrix to Image
im.segmented <- Image(im.segmented, colormode=Color)
par(mfrow=c(1,2))
plot(im.segmented, title= "reconstitute image using k-mean clustering")
text(470, -50, "k-means reconstituted image ", cex = 1.5)
plot(im, title= "original image")
text(500, -50, " Original image", cex = 1.5)


}

get_kmean_image(file = "basales.jpg", k = 6)